{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "initial_id",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-22T07:45:52.164603Z",
     "start_time": "2025-06-22T07:45:38.054576Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 导入所需的库\n",
    "from tensorflow.keras.datasets import imdb  # 导入IMDB数据集\n",
    "import numpy as np  # 导入numpy用于数组操作\n",
    "\n",
    "# 加载IMDB数据集\n",
    "# num_words=10000 表示我们只保留训练数据中最常见的10000个单词\n",
    "# 返回的是两个元组，分别包含训练数据和测试数据\n",
    "# train_data和test_data包含评论文本（已转换为整数序列）\n",
    "# train_labels和test_labels包含评论的情感标签（0表示负面，1表示正面）\n",
    "(train_data, train_labels), (test_data, test_labels) = imdb.load_data(\n",
    "    num_words=10000)\n",
    "# 将标签数据转换为numpy数组\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9146bf6a94106bc1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-22T07:48:10.412536Z",
     "start_time": "2025-06-22T07:48:10.408246Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(numpy.ndarray, numpy.ndarray, numpy.ndarray, numpy.ndarray)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 检查数据集中各个组件的类型\n",
    "# 确认所有数据都是numpy数组类型\n",
    "type(train_data), type(train_labels), type(test_data), type(test_labels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "33d63c4a3d42bc0a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-22T07:48:40.619903Z",
     "start_time": "2025-06-22T07:48:40.615157Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((25000,), (25000,), (25000,), (25000,))"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看数据集的形状\n",
    "# 训练集和测试集各有25000条评论\n",
    "# 每个数组的shape为(25000,)，表示一维数组，包含25000个元素\n",
    "train_data.shape, train_labels.shape, test_data.shape, test_labels.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f1ed8826322d9f49",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-22T07:49:23.258239Z",
     "start_time": "2025-06-22T07:49:21.694832Z"
    }
   },
   "outputs": [],
   "source": [
    "word_index = imdb.get_word_index()  #←---- word_index是一个将单词映射为整数索引的字典\n",
    "reverse_word_index = dict(\n",
    "    [(value, key) for (key, value) in word_index.items()])  #←----将字典的键和值交换，将整数索引映射为单词\n",
    "decoded_review = \" \".join(\n",
    "    [reverse_word_index.get(i - 3, \"?\") for i in train_data[0]])  #←----对评论解码。注意，索引减去了3，因为0、1、2分别是为“padding”（填充）、“start of sequence”（序列开始）、“unknown”（未知词）保留的索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e52b6cbaa21b2d8a",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-22T07:49:37.865900Z",
     "start_time": "2025-06-22T07:49:37.861603Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "? this film was just brilliant casting location scenery story direction everyone's really suited the part they played and you could just imagine being there robert ? is an amazing actor and now the same being director ? father came from the same scottish island as myself so i loved the fact there was a real connection with this film the witty remarks throughout the film were great it was just brilliant so much that i bought the film as soon as it was released for ? and would recommend it to everyone to watch and the fly fishing was amazing really cried at the end it was so sad and you know what they say if you cry at a film it must have been good and this definitely was also ? to the two little boy's that played the ? of norman and paul they were just brilliant children are often left out of the ? list i think because the stars that play them all grown up are such a big profile for the whole film but these children are amazing and should be praised for what they have done don't you think the whole story was so lovely because it was true and was someone's life after all that was shared with us all\n"
     ]
    }
   ],
   "source": [
    "# 打印解码后的第一条评论\n",
    "# 可以看到评论内容是一段完整的电影评论文本\n",
    "# 其中的\"?\"表示词汇表之外的单词（未知词）\n",
    "print(decoded_review)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e654767ab6138faf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def vectorize_sequences(sequences, dimension=10000):\n",
    "    # 创建一个形状为 (len(sequences), dimension) 的零矩阵\n",
    "    results = np.zeros((len(sequences), dimension))\n",
    "    for i, sequence in enumerate(sequences):\n",
    "        # 将每个整数序列转换为多热向量\n",
    "        # sequence中的每个整数表示一个单词的索引\n",
    "        # 将对应位置设为1，表示该单词在评论中出现\n",
    "        results[i, sequence] = 1.\n",
    "    return results\n",
    "\n",
    "# 将训练数据和测试数据转换为多热向量\n",
    "x_train = vectorize_sequences(train_data)\n",
    "x_test = vectorize_sequences(test_data)\n",
    "\n",
    "# 将标签数据转换为numpy数组\n",
    "y_train = np.array(train_labels)\n",
    "y_test = np.array(test_labels)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "dc8b9a73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.float32(1.0)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train = np.asarray(train_labels).astype(\"float32\")\n",
    "y_test = np.asarray(test_labels).astype(\"float32\")\n",
    "y_train[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "55e1defd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "e:\\Programs\\Python312\\Lib\\site-packages\\keras\\src\\layers\\core\\dense.py:93: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "\u001B[1m49/49\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m9s\u001B[0m 149ms/step - accuracy: 0.7371 - loss: 0.5573 - val_accuracy: 0.8798 - val_loss: 0.3437\n",
      "Epoch 2/20\n",
      "\u001B[1m49/49\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 14ms/step - accuracy: 0.9041 - loss: 0.2859 - val_accuracy: 0.8782 - val_loss: 0.3026\n",
      "Epoch 3/20\n",
      "\u001B[1m49/49\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 17ms/step - accuracy: 0.9264 - loss: 0.2141 - val_accuracy: 0.8863 - val_loss: 0.2858\n",
      "Epoch 4/20\n",
      "\u001B[1m49/49\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 14ms/step - accuracy: 0.9359 - loss: 0.1833 - val_accuracy: 0.8835 - val_loss: 0.2914\n",
      "Epoch 5/20\n",
      "\u001B[1m49/49\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 14ms/step - accuracy: 0.9458 - loss: 0.1592 - val_accuracy: 0.8796 - val_loss: 0.3079\n",
      "Epoch 6/20\n",
      "\u001B[1m49/49\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 15ms/step - accuracy: 0.9526 - loss: 0.1406 - val_accuracy: 0.8732 - val_loss: 0.3336\n",
      "Epoch 7/20\n",
      "\u001B[1m49/49\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 13ms/step - accuracy: 0.9557 - loss: 0.1262 - val_accuracy: 0.8756 - val_loss: 0.3379\n",
      "Epoch 8/20\n",
      "\u001B[1m49/49\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 13ms/step - accuracy: 0.9642 - loss: 0.1100 - val_accuracy: 0.8736 - val_loss: 0.3549\n",
      "Epoch 9/20\n",
      "\u001B[1m49/49\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 13ms/step - accuracy: 0.9674 - loss: 0.1030 - val_accuracy: 0.8725 - val_loss: 0.3752\n",
      "Epoch 10/20\n",
      "\u001B[1m49/49\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 14ms/step - accuracy: 0.9714 - loss: 0.0902 - val_accuracy: 0.8708 - val_loss: 0.3946\n",
      "Epoch 11/20\n",
      "\u001B[1m49/49\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 18ms/step - accuracy: 0.9732 - loss: 0.0837 - val_accuracy: 0.8672 - val_loss: 0.4130\n",
      "Epoch 12/20\n",
      "\u001B[1m49/49\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 14ms/step - accuracy: 0.9760 - loss: 0.0760 - val_accuracy: 0.8585 - val_loss: 0.4556\n",
      "Epoch 13/20\n",
      "\u001B[1m49/49\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 19ms/step - accuracy: 0.9792 - loss: 0.0680 - val_accuracy: 0.8637 - val_loss: 0.4831\n",
      "Epoch 14/20\n",
      "\u001B[1m49/49\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 14ms/step - accuracy: 0.9809 - loss: 0.0607 - val_accuracy: 0.8617 - val_loss: 0.4833\n",
      "Epoch 15/20\n",
      "\u001B[1m49/49\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 13ms/step - accuracy: 0.9858 - loss: 0.0511 - val_accuracy: 0.8521 - val_loss: 0.5418\n",
      "Epoch 16/20\n",
      "\u001B[1m49/49\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 13ms/step - accuracy: 0.9864 - loss: 0.0462 - val_accuracy: 0.8571 - val_loss: 0.5343\n",
      "Epoch 17/20\n",
      "\u001B[1m49/49\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 13ms/step - accuracy: 0.9898 - loss: 0.0398 - val_accuracy: 0.8501 - val_loss: 0.5882\n",
      "Epoch 18/20\n",
      "\u001B[1m49/49\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 13ms/step - accuracy: 0.9928 - loss: 0.0337 - val_accuracy: 0.8589 - val_loss: 0.5803\n",
      "Epoch 19/20\n",
      "\u001B[1m49/49\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 14ms/step - accuracy: 0.9935 - loss: 0.0297 - val_accuracy: 0.8577 - val_loss: 0.6322\n",
      "Epoch 20/20\n",
      "\u001B[1m49/49\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 12ms/step - accuracy: 0.9929 - loss: 0.0303 - val_accuracy: 0.8581 - val_loss: 0.6378\n"
     ]
    }
   ],
   "source": [
    "from keras import models\n",
    "from keras import layers\n",
    "\n",
    "# 构建三层模型\n",
    "model = models.Sequential()\n",
    "model.add(layers.Dense(16, activation=\"relu\", input_shape=(10000,)))#input_shape 输入的维度\n",
    "model.add(layers.Dense(16, activation=\"relu\"))#16个神经元\n",
    "model.add(layers.Dense(1, activation=\"sigmoid\"))#sigmoid 输出0-1之间的值\n",
    "\n",
    "# 编译模型\n",
    "model.compile(optimizer=\"rmsprop\",#优化器\n",
    "              loss=\"binary_crossentropy\",#损失函数\n",
    "              metrics=[\"accuracy\"])#评估指标\n",
    "\n",
    "# 训练模型\n",
    "history = model.fit(\n",
    "    x_train,\n",
    "    y_train,\n",
    "    epochs=20,#训练20次\n",
    "    batch_size=512,#每次训练512个样本\n",
    "    validation_data=(x_test, y_test)#验证集\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "deade079",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "epochs = range(1, len(loss) + 1)\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 设置中文字体为黑体\n",
    "plt.rcParams['axes.unicode_minus'] = False    # 正常显示负号\n",
    "             # 设置图例字体大小\n",
    "\n",
    "plt.figure(figsize=(8,6))\n",
    "plt.plot(epochs, loss, 'bo-', label='训练损失')\n",
    "plt.plot(epochs, val_loss, 'ro-', label='验证损失')\n",
    "plt.title('训练损失和验证损失')\n",
    "plt.xlabel('轮数')\n",
    "plt.ylabel('损失')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# 绘制训练精度和验证精度\n",
    "acc = history.history['accuracy']\n",
    "val_acc = history.history['val_accuracy']\n",
    "plt.figure(figsize=(8,6))\n",
    "plt.plot(epochs, acc, 'bo-', label='训练精度')\n",
    "plt.plot(epochs, val_acc, 'ro-', label='验证精度')\n",
    "plt.title('训练精度和验证精度')\n",
    "plt.xlabel('轮数')\n",
    "plt.ylabel('精度')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f44cdf71",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3\n",
      "\u001B[1m49/49\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m6s\u001B[0m 124ms/step - accuracy: 0.9958 - loss: 0.0167 - val_accuracy: 0.8453 - val_loss: 0.8178\n",
      "Epoch 2/3\n",
      "\u001B[1m49/49\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 15ms/step - accuracy: 0.9972 - loss: 0.0147 - val_accuracy: 0.8540 - val_loss: 0.8096\n",
      "Epoch 3/3\n",
      "\u001B[1m49/49\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 13ms/step - accuracy: 0.9976 - loss: 0.0116 - val_accuracy: 0.8535 - val_loss: 0.8357\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 为防止过拟合，调整训练轮数为较小值（如4），并重新训练模型\n",
    "history = model.fit(\n",
    "    x_train,\n",
    "    y_train,\n",
    "    epochs=3,  # 训练4次，减少过拟合风险\n",
    "    batch_size=512,\n",
    "    validation_data=(x_test, y_test)\n",
    ")\n",
    "\n",
    "\n",
    "# 获取新的训练过程中的损失和精度\n",
    "loss = history.history['loss']\n",
    "val_loss = history.history['val_loss']\n",
    "acc = history.history['accuracy']\n",
    "val_acc = history.history['val_accuracy']\n",
    "epochs = range(1, len(loss) + 1)\n",
    "\n",
    "# 绘制验证损失\n",
    "plt.figure(figsize=(8,6))\n",
    "plt.plot(epochs, val_loss, 'ro-', label='验证损失')\n",
    "plt.title('验证损失')\n",
    "plt.xlabel('轮数')\n",
    "plt.ylabel('损失')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# 绘制验证精度\n",
    "plt.figure(figsize=(8,6))\n",
    "plt.plot(epochs, val_acc, 'ro-', label='验证精度')\n",
    "plt.title('验证精度')\n",
    "plt.xlabel('轮数')\n",
    "plt.ylabel('精度')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "id": "1821fefc",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-23T09:59:57.599192Z",
     "start_time": "2025-06-23T09:59:55.683082Z"
    }
   },
   "source": [
    "import torch\n",
    "print(torch.__version__)\n",
    "print(torch.cuda.is_available())  # 应该输出 False\n"
   ],
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.7.1+cpu\n",
      "False\n"
     ]
    }
   ],
   "execution_count": 1
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
